Case Studies and Applications of Explainability and Interpretability in AI Models

Within the field of artificial intelligence, the phrases “explainability,” “interpretability,” and “transparency” lack a clear meaning and are sometimes used synonymically. A transparent artificial intelligence system fosters responsibility by allowing stakeholders to check and audit its decision-making processes, spot prejudice or unfairness, and verify that the system is running in line with legal and ethical standards. Showing users and authorities how AI systems achieved certain outcomes in relevant environments becomes vital when they must confirm AI findings. Showing people what is happening and accessible for inspection by authorities is the essence of explainability.